{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T14:40:31Z","timestamp":1743345631626,"version":"3.40.3"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01245-0","type":"journal-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T22:02:20Z","timestamp":1726092140000},"page":"694-702","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Screening Patient Misidentification Errors Using a Deep Learning Model of Chest Radiography: A Seven Reader Study"],"prefix":"10.1007","volume":"38","author":[{"given":"Kiduk","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyungjin","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujeong","family":"Eo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeeyoung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihye","family":"Yun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yura","family":"Ahn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joon Beom","family":"Seo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gil-Sun","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3438-2217","authenticated-orcid":false,"given":"Namkug","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"1245_CR1","doi-asserted-by":"publisher","first-page":"941","DOI":"10.2214\/AJR.15.14720","volume":"205","author":"EI Rubio","year":"2015","unstructured":"Rubio EI, Hogan L: Time-Out: It's Radiology's Turn\u2014Incidence of Wrong-Patient or Wrong-Study Errors. American Journal of Roentgenology 205:941-946, 2015","journal-title":"American Journal of Roentgenology"},{"key":"1245_CR2","doi-asserted-by":"publisher","first-page":"337","DOI":"10.2214\/AJR.14.13339","volume":"205","author":"G Sadigh","year":"2015","unstructured":"Sadigh G, Loehfelm T, Applegate KE, Tridandapani S: JOURNAL CLUB: Evaluation of Near-Miss Wrong-Patient Events in Radiology Reports. American Journal of Roentgenology 205:337-343, 2015","journal-title":"American Journal of Roentgenology"},{"key":"1245_CR3","doi-asserted-by":"crossref","unstructured":"Beyea SC: Patient identification--a crucial aspect of patient safety, 2003","DOI":"10.1016\/S0001-2092(06)60757-6"},{"key":"1245_CR4","doi-asserted-by":"crossref","unstructured":"Papadakis M, Meiwandi A, Grzybowski A: The WHO safer surgery checklist time out procedure revisited: Strategies to optimise compliance and safety. International Journal of Surgery 69, 2019","DOI":"10.1016\/j.ijsu.2019.07.006"},{"key":"1245_CR5","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1016\/j.annemergmed.2009.11.017","volume":"55","author":"PL Henneman","year":"2010","unstructured":"Henneman PL, Fisher DL, Henneman EA, Pham TA, Campbell MM, Nathanson BH: Patient Identification Errors Are Common in a Simulated Setting. Annals of Emergency Medicine 55:503-509, 2010","journal-title":"Annals of Emergency Medicine"},{"key":"1245_CR6","doi-asserted-by":"publisher","DOI":"10.2196\/11472","volume":"7","author":"B Jeon","year":"2019","unstructured":"Jeon B, et al.: A Facial Recognition Mobile App for Patient Safety and Biometric Identification: Design, Development, and Validation. JMIR Mhealth Uhealth 7:e11472, 2019","journal-title":"JMIR Mhealth Uhealth"},{"key":"1245_CR7","doi-asserted-by":"publisher","first-page":"2391","DOI":"10.1002\/mp.12241","volume":"44","author":"E Silverstein","year":"2017","unstructured":"Silverstein E, Snyder M: Implementation of facial recognition with Microsoft Kinect v2 sensor for patient verification. Medical Physics 44:2391-2399, 2017","journal-title":"Medical Physics"},{"key":"1245_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2020.104180","volume":"141","author":"S Ampamya","year":"2020","unstructured":"Ampamya S, Kitayimbwa JM, Were MC: Performance of an open source facial recognition system for unique patient matching in a resource-limited setting. International Journal of Medical Informatics 141:104180, 2020","journal-title":"International Journal of Medical Informatics"},{"key":"1245_CR9","doi-asserted-by":"crossref","unstructured":"Morishita J, Katsuragawa S, Sasaki Y, Doi K: Potential usefulness of biological fingerprints in chest radiographs for automated patient recognition and identification1. Academic Radiology 11:309\u2013315, 2004","DOI":"10.1016\/S1076-6332(03)00655-X"},{"key":"1245_CR10","doi-asserted-by":"crossref","unstructured":"Morishita J, Katsuragawa S, Kondo K, Doi K: An automated patient recognition method based on an image-matching technique using previous chest radiographs in the picture archiving and communication system environment. Medical Physics 28:1093\u20131097, 2001","DOI":"10.1118\/1.1373403"},{"key":"1245_CR11","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1016\/j.acra.2013.04.006","volume":"20","author":"EF Kao","year":"2013","unstructured":"Kao EF, Lin W-C, Jaw T-S, Liu G-C, Wu J-S, Lee C-N: Automated Patient Identity Recognition by Analysis of Chest Radiograph Features. Academic Radiology 20:1024-1031, 2013","journal-title":"Academic Radiology"},{"key":"1245_CR12","doi-asserted-by":"crossref","unstructured":"Raghu Vineet K, Weiss J, Hoffmann U, Aerts Hugo JWL, Lu Michael T: Deep Learning to Estimate Biological Age From Chest Radiographs. JACC: Cardiovascular Imaging 14:2226\u20132236, 2021","DOI":"10.1016\/j.jcmg.2021.01.008"},{"key":"1245_CR13","doi-asserted-by":"crossref","unstructured":"He H, et al.: Model and predict age and sex in healthy subjects using brain white matter features: a deep learning approach. Proc. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI): City","DOI":"10.1109\/ISBI52829.2022.9761684"},{"key":"1245_CR14","doi-asserted-by":"publisher","first-page":"e406","DOI":"10.1016\/S2589-7500(22)00063-2","volume":"4","author":"JW Gichoya","year":"2022","unstructured":"Gichoya JW, et al.: AI recognition of patient race in medical imaging: a modelling study. The Lancet Digital Health 4:e406-e414, 2022","journal-title":"The Lancet Digital Health"},{"key":"1245_CR15","doi-asserted-by":"publisher","first-page":"14851","DOI":"10.1038\/s41598-022-19045-3","volume":"12","author":"K Packh\u00e4user","year":"2022","unstructured":"Packh\u00e4user K, G\u00fcndel S, M\u00fcnster N, Syben C, Christlein V, Maier A: Deep learning-based patient re-identification is able to exploit the biometric nature of medical chest X-ray data. Scientific Reports 12:14851, 2022","journal-title":"Scientific Reports"},{"key":"1245_CR16","unstructured":"Donaldson MS, Corrigan JM, Kohn LT: To err is human: building a safer health system, 2000"},{"key":"1245_CR17","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1148\/radiol.2021204164","volume":"302","author":"J Choe","year":"2021","unstructured":"Choe J, et al.: Content-based Image Retrieval by Using Deep Learning for Interstitial Lung Disease Diagnosis with Chest CT. Radiology 302:187-197, 2021","journal-title":"Radiology"},{"key":"1245_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102894","volume":"89","author":"K Cho","year":"2023","unstructured":"Cho K, et al.: MuSiC-ViT: A multi-task Siamese convolutional vision transformer for differentiating change from no-change in follow-up chest radiographs. Medical Image Analysis 89:102894, 2023","journal-title":"Medical Image Analysis"},{"key":"1245_CR19","unstructured":"Irvin J, et al.: Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proc. Proceedings of the AAAI conference on artificial intelligence: City"},{"key":"1245_CR20","unstructured":"Wu JT, et al.: Chest ImaGenome dataset for clinical reasoning. arXiv preprint, 2021"},{"key":"1245_CR21","unstructured":"Khosla P, et al.: Supervised contrastive learning. Proc. Advances in neural information processing systems: City"},{"key":"1245_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106705","volume":"220","author":"KD Kim","year":"2022","unstructured":"Kim KD, et al.: Enhancing deep learning based classifiers with inpainting anatomical side markers (L\/R markers) for multi-center trials. Computer Methods and Programs in Biomedicine 220:106705, 2022","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"1245_CR23","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D: Grad-cam: Visual explanations from deep networks via gradient-based localization. Proc. Proceedings of the IEEE international conference on computer vision: City"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01245-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01245-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01245-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T14:16:14Z","timestamp":1743344174000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01245-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"references-count":23,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,4]]}},"alternative-id":["1245"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01245-0","relation":{},"ISSN":["2948-2933"],"issn-type":[{"type":"electronic","value":"2948-2933"}],"subject":[],"published":{"date-parts":[[2024,9,11]]},"assertion":[{"value":"22 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 August 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective study was conducted according to the principles of the Declaration of Helsinki and according to current scientific guidelines. The study protocol was approved by the Institutional Review Board Committee (IRB) of Asan Medical Center, Seoul, Korea (IRB no.2019\u20130115, 2019\u20130321). The requirement for written informed consent was waived by the IRB because the data were analyzed retrospectively and anonymously.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"This requirement for written informed consent was waived by the IRB because the data were analyzed retrospectively and anonymously.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"This requirement for written informed consent was waived by the IRB because the data were analyzed retrospectively and anonymously.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Publish"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}